REP - Reverse Engineering of Genetic Regulatory Networks

Background

One of the major breakthroughs in todays molecular cell biology has been made by the new microarray technologies which allow simultaneous measurements of gene expression levels at genomic scales. These and other high-throughput technologies, which provide biological information on RNA levels, protein function and distribution, and metabolite pools, can be used to improve our understanding of the mechanisms underlying the regulatory systems of the cell. The main idea is to iteratively use advanced computational techniques in order to generate hypotheses about the regulatory network under consideration. The produced hypotheses guide the design of future biological experiments which, in turn, allow to verify and revise the existing system model. Reverse engineering of regulatory networks or parts of them currently represents a demanding problem at the forefront of Computational Science and Biology.

Goals

This project aims at the development, application, and evaluation of new computational tools for the analysis of molecular and profiling experiments. It is a cross-disciplinary project that brings together scientists at ETH Zurich with expertise in Biology, Statistics, Computer Science, and Information Technology.

Results

In the course of this project, we have developed methods for advanced analysis of gene expression data and additional biological high-throughput measurements. These results include a new form of Gaussian graphical models for the identification of gene regulatory networks [wzvf2004a], an empirical biclustering comparison study including an exact reference algorithm [pbzw2006a] and a flexible biclustering framework based on an hybrid Evolutionary Algorithm [bpz2004a]. Using this framework two important issues in data integration were addressed: (i) how to jointly analyze multiple gene expression data sets stemming from different experimental setups, different labs or different measurement technologies [bz2005a], (ii) how to integrate additional types of high-throughput data with transcriptomic data for a joint cluster analysis [cbz2006a]. See the publication list for further studies resulting from REP.

Additionally, a user-friendly biclustering tool was developed which includes several current biclustering algorithms [bbpz2006a].

Collaborators

• Anja Wille and Peter Bühlmann, Seminar of Statistics, ETH Zurich.
• Philip Zimmermann, Daniel Schöner, Lars Hennig and Wilhelm Gruissem Plant Science Laboratory, ETH Zurich
• Markus Friberg, Institute of Computational Sciences, ETH Zurich.

Funding

ETH Zurich, SEP Program, Project TH-8/02-2

Materials

• BicAT: User-friendly Biclustering Toolbox in Java including several current algorithms.
• Supplementary material for the biclustering comparison study presented in [pbzw2006a].
• Supplementary material for the study integrating multiple types of high-throughput data [cbz2006a].

Publications (with co-authors from the Systems Optimization Group)

[1 — bzfz2008a]

S. Bleuler, P. Zimmermann, M. Friberg, and E. Zitzler. Discovering Trends in Gene Expression Data Using a Hybrid Evolutionary Algorithm. Algorithmic Operations Research, 3(2), 2008. (bibtex)

[2 — bleu2007a]

S. Bleuler. Search Heuristics for Module Identification from Biological High-Throughput Data. PhD thesis, ETH Zurich, Switzerland, 2007. (PDF) (bibtex) (online access)

[3 — sbbw2007a]

D. Schöner, S. Barkow, S. Bleuler, A. Wille, P. Zimmermann, P. Bühlmann, W. Gruissem, and E. Zitzler. Network Analysis of Systems Elements. In S. Baginsky and A. R. Fernie, editors, Plant Systems Biology, pages 331–351. Birkhäuser, Basel, Switzerland, 2007. (bibtex) (online access)

[4 — cbz2006a]

M. Calonder, S. Bleuler, and E. Zitzler. Module Identification from Heterogeneous Biological Data Using Multiobjective Evolutionary Algorithms. In T. P. Runarsson et al., editors, Conference on Parallel Problem Solving from Nature (PPSN IX), volume 4193 of LNCS, pages 573–582. Springer, 2006. (PDF) (bibtex) (online access) (suppl. material)

[5 — bbpz2006a]

S. Barkow, S. Bleuler, A. Prelic, P. Zimmermann, and E. Zitzler. BicAT: a Biclustering Analysis Toolbox. Bioinformatics, 22(10):1282–1283, 2006. (bibtex) (online access) (suppl. material)

[6 — pbzw2006a]

A. Prelic, S. Bleuler, P. Zimmermann, A. Wille, P. Bühlmann, W. Gruissem, L. Hennig, L. Thiele, and E. Zitzler. A Systematic Comparison and Evaluation of Biclustering Methods for Gene Expression Data. Bioinformatics, 22(9):1122–1129, 2006. (bibtex) (online access) (suppl. material)

[7 — pbzw2006b]

A. Prelic, S. Bleuler, P. Zimmermann, A. Wille, P. Bühlmann, W. Gruissem, L. Hennig, L. Thiele, and E. Zitzler. Comparison of Biclustering Methods: A Systematic Comparison and Evaluation of Biclustering Methods for Gene Expression Data. TIK Report 227, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, February 2006. (PDF) (bibtex)

[8 — bzfw2006a]

S. Bleuler, P. Zimmermann, M. Friberg, A. Wille, S. Barkow, D. Brockhoff, D. Schöner, L. Hennig, P. Bühlmann, W. Gruissem, L. Thiele, and E. Zitzler. Cluster Analysis of Multiple Time Course Data Sets. TIK Report 241, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, 2006. (bibtex)

[9 — bz2005a]

S. Bleuler and E. Zitzler. Order Preserving Clustering over Multiple Time Course Experiments. In EvoWorkshops 2005, volume 3449 of LNCS, pages 33–43. Springer, 2005. (PDF) (bibtex) (online access)

[10 — wzvf2004a]

A. Wille, P. Zimmermann, E. Vranova, A. Fürholz, O. Laule, S. Bleuler, L. Hennig, A. Prelic, P. von Rohr, L. Thiele, E. Zitzler, W. Gruissem, and P. Bühlmann. Sparse Graphical Gaussian Modeling of the Isoprenoid Gene Network in Arabidopsis thaliana. Genome Biol, 5(11):R92, 2004. (PDF) (bibtex) (online access)

[11 — bpz2004a]

S. Bleuler, A. Prelic, and E. Zitzler. An EA Framework for Biclustering of Gene Expression Data. In Congress on Evolutionary Computation (CEC 2004), pages 166–173, Piscataway, NJ, 2004. IEEE. (PDF) (bibtex)

[12 — hzr2003a]

R. Hubley, E. Zitzler, and J. Roach. Evolutionary algorithms for the selection of single nucleotide polymorphisms. BMC Bioinformatics, 4(30), 2003. (bibtex) (online access)

[13 — bltz2003a]

S. Bleuler, M. Laumanns, L. Thiele, and E. Zitzler. PISA—A Platform and Programming Language Independent Interface for Search Algorithms. In C. M. Fonseca et al., editors, Conference on Evolutionary Multi-Criterion Optimization (EMO 2003), volume 2632 of LNCS, pages 494–508, Berlin, 2003. Springer. (PDF) (bibtex) (online access) (suppl. material)

[14 — hzsr2002a]

R. Hubley, E. Zitzler, A. Siegel, and J. Roach. Multiobjective Genetic Marker Selection. In Advances in Nature-Inspired Computation: The PPSN VII Workshops, pages 32–33. University of Reading, UK, September 2002. (PDF) (bibtex)

[15 — bltz2002a]

S. Bleuler, M. Laumanns, L. Thiele, and E. Zitzler. PISA — A Platform and Programming Language Independent Interface for Search Algorithms. TIK Report 154, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, October 2002. (PDF) (bibtex)